Skill UI

This utility facilitates the creation and management of Dynamic Agentic Architecture (DAA) agents. Unlike static systems, DAA agents are designed to continuously learn from interactions, adapt their behavior based on performance metrics, and propagate knowledge across multiple agents. It provides comprehensive tools for agent creation, monitoring learning progress, checking performance, manual adaptation, and knowledge sharing, ensuring robust and evolving AI workflows.

A comprehensive guide to idiomatic and robust Dart/Flutter patterns. Covers essential topics including null safety, immutable state management (Freezed), advanced state solutions (BLoC, Riverpod, Provider), reactive routing with GoRouter, secure networking using Dio, and implementing Clean Architecture principles. Ideal for setting up new, scalable Flutter projects.

This tool guides the creation of actionable monitoring dashboards for observability platforms like Grafana and SigNoz. Instead of merely displaying raw metrics, it focuses on structuring data to answer critical operational questions: Is the system healthy? Where are the bottlenecks? What changes occurred? And what action should be taken? It transforms raw metrics into decision-making tools.

Build a production-ready, AI-powered data collection agent for any public source (job boards, prices, news, etc.). This agent automatically scrapes data, enriches it using free LLMs (like Gemini Flash), and stores the results in databases such as Notion, Sheets, or Supabase. It runs on a free schedule using GitHub Actions and learns from user feedback, making it ideal for continuous data monitoring and tracking.

A comprehensive methodology for accelerating large-scale data ingestion, ETL processes, backfilling, and data warehouse loading. It guides users in identifying and optimizing bottlenecks—such as source extraction, network transfer, and loading speed—while strictly ensuring data correctness and maintaining clear accounting checkpoints throughout the pipeline.

A comprehensive guide to safe and reversible database migration patterns. Covers zero-downtime schema changes, data backfilling, and rollbacks for major databases like PostgreSQL and MySQL, and popular ORMs (Prisma, Drizzle, Django). Learn techniques like concurrent indexing and the expand-contract pattern to ensure production stability.

Automates the scaffolding of a complete Aggregate Root structure following Domain-Driven Design (DDD) principles. It generates necessary boilerplate files including the core entity, immutable value objects, repository interface, domain events, and unit test stubs within a specified bounded context. This tool is essential for modeling new business concepts or extending existing domain models while maintaining strong architectural integrity.

This tool scaffolds a complete directory structure for a new Domain-Driven Design (DDD) Bounded Context. It automatically generates the standard layout, including `domain`, `application`, and `infrastructure` layers, and populates index files for clean module exports. Ideal for starting new microservices, splitting monoliths, or structuring fresh modules.

This tool validates the structural integrity of a Domain-Driven Design (DDD) codebase. It automatically detects cross-context import violations, checks for broken aggregate invariant enforcement, and verifies adherence to established patterns (like repository and event naming conventions). Essential for CI/CD gates and large-scale codebase auditing to prevent architectural leakage.

A comprehensive guide detailing security patterns and audit checklists for Automated Market Maker (AMM) smart contracts in DeFi. Covers critical vulnerabilities such as flash loans, slippage, reentrancy attacks, price manipulation, and integer overflow/underflow. Essential for reviewing major DeFi protocols like Uniswap.

A deterministic 'Stop hook' designed to enforce session hygiene for AI agents. It checks machine-verifiable facts such as file modification timestamps of learning logs, disk space, and predefined rationalization patterns. This gate ensures that complex tasks lead to captured knowledge, acting as a mechanical safeguard complementing AI self-auditing.

This tool scans project dependencies to detect known Common Vulnerabilities and Exposures (CVEs) and outdated packages. It is essential for security auditing of third-party packages, mandatory before major releases, or immediately after dependency updates. It helps teams assess risk levels (critical, high, moderate) and can even attempt automatic fixes via the CLI.